Life & fun · alternatives · 2026
24 alternatives to We built an OCR server that can process 270 dense images/s on a 5090
TurboOCR, >200 img/s OmnidocBench. TensorRT FP16, PP-OCRv6, HTTP + gRPC - aiptimizer/TurboOCR
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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DeepSeek-OCR▲350Advanced visual-text compression technology
2025 · huggingface.co · its alternatives →
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Pure Javascript OCR for more than 100 Languages 📖🎉🖥 - naptha/tesseract.js
2016 · github.com · its alternatives →
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I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac. I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files. Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as…
Apr 2026 · github.com · its alternatives →
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I built OCR Arena as a free playground for the community to compare leading foundation VLMs and open-source OCR models side-by-side. Upload any doc, measure accuracy, and (optionally) vote for the models on a public leaderboard. It currently has Gemini 3, dots.ocr, DeepSeek, GPT5, olmOCR 2, Qwen, and a few others. If there's any others you'd like included, let me know!
Nov 2025 · ocrarena.ai · its alternatives →
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This was not supposed to become a product. When PaddleOCR-VL-1.6 dropped, independent benchmarks put it at the top of document parsing models. I had to try it. I needed a provider, but there simply isn't one ready for production that I would trust. So i set one up myself. I assumed that even after getting it running, serving a vision-language model would be expensive. It turns out the opposite is true. Once I had it running properly, the cost was absurdly low. At proper GPU utilization, the cost is only around $1 per 1,000 pages. The nearest competitors are either much lower quality (Azure…
Jul 2026 · openparser.dev · its alternatives →
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Vision models have been gaining popularity as a replacement for traditional OCR. Especially with Gemini 2.0 becoming cost competitive with the cloud platforms. We've been continuously evaluating different models since we released the Zerox package last year (https://github.com/getomni-ai/zerox). And we wanted to put some numbers behind it. So we’re open sourcing our internal OCR benchmark + evaluation datasets. Full writeup + data explorer here: https://getomni.ai/ocr-benchmark Github: https://github.com/getomni-ai/benchmark Huggingface:…
2025 · getomni.ai · its alternatives →
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Last week was big for open source LLMs. We got: - Qwen 2.5 VL (72b and 32b) - Gemma-3 (27b) - DeepSeek-v3-0324 And a couple weeks ago we got the new mistral-ocr model. We updated our OCR benchmark to include the new models. We evaluated 1,000 documents for JSON extraction accuracy. Major takeaways: - Qwen 2.5 VL (72b and 32b) are by far the most impressive. Both landed right around 75% accuracy (equivalent to GPT-4o’s performance). Qwen 72b was only 0.4% above 32b. Within the margin of error. - Both Qwen models passed mistral-ocr (72.2%), which is specifically trained for OCR. - Gemma-3…
2025 · github.com · its alternatives →
- 9AI
Hello HN! In a recent "Ask HN: What are you working on?" thread, I mentioned I was working on OCRing a large book: https://news.ycombinator.com/item?id=41971614 The post generated some interest so I thought I would keep HN posted. The book is Saint-Simon’s Memoirs -- an invaluable historical account of the French court under Louis XIV, full of wit, sharp observations, and of incredible literary value. I'm OCRing the edition of reference made between 1879-1930, that contains a lot of comments and footnotes: 45 volumes, ~27,000 pages. Here's a link to a blog post that describes…
2024 · blog.medusis.com · its alternatives →
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🔍 Better text detection by combining multiple OCR engines (EasyOCR, Tesseract, and Pororo) with 🧠 LLM. - junhoyeo/BetterOCR
2023 · github.com · its alternatives →
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It's our new text-to-image model: a 9.3B single-stream diffusion transformer trained entirely from scratch. We focused heavily on controllability through structured JSON prompts, with strong text rendering, spatial awareness through bounding box guidance, and color palette control. It has the best text rendering of any open-weight model we've tested so far, and the NF4 quantized checkpoint runs on a single 24GB GPU. For more technical details and examples see our blog post: https://ideogram.ai/blog/ideogram-4.0/ We will be happy to answer any questions :)
Jun 2026 · github.com · its alternatives →
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A Python wrapper for the tesseract-ocr API. Contribute to sirfz/tesserocr development by creating an account on GitHub.
2016 · github.com · its alternatives →
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2016 · github.com · its alternatives →
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High performance storage engine for efficient LLM inference and GPU Training.
Sep 2026 · theopenlake.com · its alternatives →
- 15OO
Built this because people working with large document sets had no free tool that handled batch processing cleanly. Tesseract is free and runs locally. For anything that needs more accuracy — Google Vision, Gemini, or any OpenRouter model — you bring your own API key. No subscription, no markup on your usage. Export as TXT, JSON, XML or PDF. AI engines support custom prompts so you can translate, extract form fields, or get structured output in one step. App: https://onlineocrfree.qzz.io Source: https://github.com/naimurhasan/online-ocr-free
Mar 2026 · onlineocrfree.qzz.io · its alternatives →
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Kaizen OCR▲31Fully offline OCR with 100+ languages support. Full Privacy
2025 · kaizen-apps.com · its alternatives →
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Enterprise-grade image generation at unbeatable prices
2025 · tokenfactory.nebius.com · its alternatives →
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Hey HN, we’re the developers of OpenLake, an open source storage engine for offloading LLM KV caches from GPU memory into a shared tier of RAM and NVMe. We built OpenLake because KV caches are outgrowing GPU memory. A single 256K token conversation on Gemma 4 31B produces approximately 43GB of KV state, more than half the memory of an 80GB H100. The problem becomes even harder across a cluster: a prefix cached on one GPU host is unavailable when the next request lands on a different GPU, forcing the new GPU to repeat work the fleet has already completed. Once the KV cache is offloaded,…
Jul 2026 · github.com · its alternatives →
- 20SS
2020 · frankbyte.com · its alternatives →
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I wanted to know how fast a 26B mixture-of-experts model could run on a desktop CPU with no GPU. Got ~40 tok/s single-stream (lossless) and ~124 batched. The surprising part was the byte budget: for this model you compress the output head (32% of per-token bytes), not the experts (16%). The writeup has the bandwidth roofline and the dead-ends; the repo has the reproducible recipe. Happy to answer questions. Repo: https://github.com/arun-prasath2005/gemma4-cpu-moe
Jun 2026 · apeg.dev · its alternatives →
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A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model. - faustomorales/keras-ocr
2019 · github.com · its alternatives →
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Quick note on how it works and how I've done my batch embedding engine IgniteMS. The whole thing runs as one process using Rust, reading input, tokenizing, packing batches, keeping the queue full. TensorRT handles inference. Python is only as a wrapper. I built it this way because when you use more than couple of GPUs, the GPUs stop being the problem. CPU cannot feed them fast enough. One A100 can go through batches faster than Python can tokenize and feed, so the GPU just sits there idle waiting for work. Most of my time went into optimizing this. At 8 GPUs that was basically the entire…
Jun 2026 · github.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →